Many different data processing systems already exist, such as Microsoft SQL Server, SAS, SPSS, and ACL. These typically allow the analysis of data that is held on a server performing the analysis or held on another server within the same data center or legal entity and to which the server performing the analysis has access. To process data held at a plurality of locations remote from each other and remote from the server performing the analysis, it is necessary first to pull the data into a common location.
There are, however, significant technical problems with processing data held and originating in different data centers, or from different legal entities. For example, should it be necessary to take a copy of the data from one data center, and move it to another data center for processing, numerous security risks are introduced. These include the possible loss of portable media drives or CDs. The movement of data can also be made problematic by the existence of agreements with trade unions, privacy concerns, reputational concerns or legal restrictions (such as the European Data Protection Directive 95/46/EC or The Swiss Federal Data Protection Act and the Swiss Federal Data Protection Ordinance). These may prevent a copy of the data from being legally taken outside the originating data center.
One example of an industry where these data processing problems are particularly evident is the financial auditing industry in which the relevant regulatory framework reinforces these problems.
By way of background, large organizations such as certain companies or partnerships, are subjected to external financial audit or other similar third-party regulatory inspections on a periodic basis. At its simplest, the purpose of an external financial audit is to validate that the prepared financial accounts of a particular organization (often referred to as the “target” of the auditing) meet the relevant accounting standards, and to provide reasonable assurance, but not absolute assurance, that the financial statements are presented fairly, in all material respects, and give a true and fair view in accordance with the prevailing financial reporting framework. Auditing firms typically audit accounts of their clients every 12 months, and may have many hundreds or thousands of clients, that is “targets”, which they audit.
When performing the external financial audit of a large company, the financial auditor may be required to sample a subset of a larger set of transactions in order to gain assurance that certain risks have not crystallized in the period under review. For example, when testing fixed assets, it is allowable for the target of the audit to include the purchase of a new building as an addition, however it is generally not allowable to include the repainting of walls within an existing building.
Identifying the two different types of transaction is relatively simple where transaction volumes are small and each item can be reviewed by a human. Where volumes increase, selecting a sample of transactions (typically up to 75) from a population of thousands of transactions appears to provide reasonable coverage of the population and thus a good probability of identifying material error. However, large companies may have millions of fixed asset movements which are required to be reviewed in each audit period. Only reviewing a small sample becomes less valuable in reducing audit risk, and also in identifying value-add findings to Audit targets.
In order to overcome this problem, data analysis can be used to perform tasks which help an auditor to determine which transactions should be subjected to a human review, and discount those which are not high risk, by analyzing an entire population of transactions regardless of the volume of data.
A number of technical problems are typically encountered when performing this type of work. These include:                Large volumes of data must be obtained for analysis by the auditor, in a cost effective way.        Data is generally only obtained during one key audit phase, limiting the times when the results of the analysis are available to the auditor.        Clients are generally opposed to the export of large volumes of data from their data centers due to significant security concerns around data loss.        
Significant time and cost is incurred by the auditing firm in performing the same types of data analysis tasks, for multiple audit targets on the same types of data. For example, Fixed Asset data generally takes a similar form at all companies, but would typically be performed as an independent data analysis task for each audit target.
It is therefore desirable to provide a data processing system and/or method that at least addresses certain of these problems.